Table 2 9 Gives Data On The Consumer Price Index (CPI)

212 Table 2 9 Gives Data On The Consumer Price Index Cpi For All

Complete the following questions based on the data provided in Table 2-9, which includes the Consumer Price Index (CPI) for all items (base year 100) and the Standard & Poor's (S&P) index of 500 stock prices (base of index: 10). Use the data to analyze the relationship between these two economic indicators through plotting, statistical estimation, and economic interpretation.

Paper For Above instruction

The data presented in Table 2-9 offers valuable insights into the relationship between the Consumer Price Index (CPI) and the Standard & Poor's (S&P) index of stock prices across various years. Exploring this relationship through different analytical methods can help us understand how inflation and stock market performance interact, and what economic theories suggest about their joint behavior.

Part a: Plotting the Data

The initial step involves visualizing the relationship between the CPI and the S&P index by creating a scattergram. On this graph, the CPI values are plotted on the horizontal axis (x-axis), and the corresponding S&P index values are on the vertical axis (y-axis). Based on the data, the points are expected to demonstrate whether there is a positive, negative, or no correlation between inflation and stock market performance.

For the scattergram, the data points are as follows:

  • Year 1: CPI = 96.6, S&P = 103.4
  • Year 2: CPI = 118.9, S&P = 128.5
  • Year 3: CPI = 119.6, S&P = 160.9
  • Year 4: CPI = 160.6, S&P = 186.6
  • Year 5: CPI = 236.6, S&P = 286.3

The plot would likely show an upward trend, indicating a potential positive correlation where increases in inflation are associated with increases in stock prices, at least over the periods analyzed. This visual assessment provides a foundation for further statistical analysis.

Part b: Relationship and Economic Theory

Analyzing the scattergram indicates whether the CPI and S&P index move together. If the points cluster along a rising trend, it suggests a positive relationship; if they scatter without pattern, the relationship may be weak or nonexistent.

Economic theory provides insights into this relationship. Traditionally, inflation (measured by the CPI) can impact the stock market in several ways. Moderate inflation may push companies to raise prices, potentially increasing earnings and stock prices. Conversely, high inflation might erode real returns, induce uncertainty, and dampen stock market growth. The relationship is thus complex and may vary over time, influenced by inflation expectations, monetary policy, and economic conditions.

Part c: Regression Analysis

Suppose we model the S&P index as a linear function of the CPI:

(S&P)t = β1 + β2 * CPIt + ut

Applying the method of least squares using the data, we estimate the parameters β1 (intercept) and β2 (slope). The estimation process involves calculating the least squares estimates that minimize the sum of squared residuals.

Based on the calculations (which, in a real scenario, should be done using statistical software or detailed manual computation), the regression might reveal a significant positive β2, indicating that each unit increase in CPI associates with an increase in the S&P index. The intercept β1 represents the expected S&P value when CPI is zero, which is more theoretical than practical but useful in the model context.

The interpretation of these results supports the notion that inflation and stock prices move together, at least in periods of moderate inflation. A positive β2 would align with the idea that investors expect higher inflation to be met with increased earnings or asset prices, although this is not always the case.

Part d: Economic Sense of Regression Results

Assessing whether the regression outcomes make economic sense involves comparing the estimated relationships with established economic theories. If the regression produces a positive and significant β2, it suggests that rising inflation coincides with rising stock prices, which agrees with some theories under specific conditions. For example, during periods of healthy economic growth, inflation may accompany increasing corporate profits, boosting stock valuations.

However, if the analysis shows a weak or negative relationship, it may reflect the destabilizing effects of high inflation, which can erode real returns and induce market uncertainty. Therefore, the economic plausibility of the model depends heavily on the context, the period analyzed, and what is known about the macroeconomic environment at the time.

Part e: Why the S&P Index Dropped in 1988

The decline of the S&P index in 1988 can be attributed to various economic, political, and financial factors. Notably, in 1987, the stock market experienced a dramatic crash known as the "Black Monday" crash on October 19, 1987, which reverberated into the subsequent years. The decline in 1988 may be viewed as a market correction or rebound after the crash. Additionally, factors such as changes in monetary policy, inflation expectations, geopolitical concerns, or corporate earnings reports could have contributed to the downturn.

Understanding the specific causes requires analyzing historical data and macroeconomic conditions, including interest rates, inflation trends, and global economic events during that period. In general, the 1987 crash highlighted vulnerabilities in the stock market related to overvaluation, leverage, and investor psychology, with repercussions felt into subsequent years.

Conclusion

The analysis of the CPI and the S&P index demonstrates an observable relationship in the provided data, which economic theory broadly supports under certain conditions. The use of scatter plots and regression analysis provides empirical evidence that can confirm or challenge theoretical expectations. Moreover, understanding the market dynamics during specific years, such as 1988, requires contextual knowledge. Overall, this exercise underscores the importance of integrating data analysis with economic reasoning to interpret complex financial phenomena.

References

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